HUSCAP logo Hokkaido Univ. logo

Hokkaido University Collection of Scholarly and Academic Papers >
Graduate School of Environmental Science / Faculty of Environmental Earth Science >
Peer-reviewed Journal Articles, etc >

Post-typhoon forest damage estimation using multiple vegetation indices and machine learning models

Files in This Item:

The file(s) associated with this item can be obtained from the following URL:

Title: Post-typhoon forest damage estimation using multiple vegetation indices and machine learning models
Authors: Chen, Xinyu Browse this author
Avtar, Ram Browse this author →KAKEN DB
Umarhadi, Deha Agus Browse this author
Louw, Albertus Stephanus Browse this author
Shrivastava, Sourabh Browse this author
Yunus, Ali P. Browse this author
Khedher, Khaled Mohamed Browse this author
Takemi, Tetsuya Browse this author →KAKEN DB
Shibata, Hideaki Browse this author →KAKEN DB
Keywords: Forest damage
Remote sensing
Vegetation indices
Multispectral classification
Issue Date: Dec-2022
Publisher: Elsevier
Journal Title: Weather and Climate Extremes
Volume: 38
Start Page: 100494
Publisher DOI: 10.1016/j.wace.2022.100494
Abstract: The frequency and intensity of typhoons have increased due to climate change. These climate change-induced disasters have caused widespread damage to forests. Evaluation of the effects of typhoons on forest ecosys-tems is often complex and challenging, mainly because of their sporadic nature. In this paper, we compared existing forest damage estimation techniques with the goal of identifying their respective advantages and suit-able use cases. We considered Hokkaido in northern Japan as a case study, where three typhoons successively struck in 2016 and led to forest destruction. Forest damage was estimated from Landsat 8 imagery by three approaches, namely using vegetation damage indices (DVDI, DNDVI and & UDelta;EVI), using supervised classification with Random Forest (RF) and Support Vector Machines (SVM) and finally by using the commercial CLASlite software with built-in methods to detect forest disturbance. Machine learning classifiers obtained the highest damage assessment accuracy, but intensive computation and complex processing steps were required. The RF and SVM classifiers gave the highest accuracies when using Fractional Cover as a predictor variable (Overall Accuracy = 80.36% in both cases, and ROC AUC values of 0.89 and 0.88, respectively.) Among the vegetation damage indices, DNDVI produced the highest accuracy (AUC = 0.85, OA = 77.68%).The most damaged areas were on the windward slopes, where forest patches were exposed to the brunt of the typhoon winds. Forest damage also peaked at the highest elevations in the study area, possibly representing exposed hilltops. Methods and findings presented in this study can help stakeholders to implement more effective forest damage monitoring after typhoons and other extreme weather events in the future.
Type: article
Appears in Collections:環境科学院・地球環境科学研究院 (Graduate School of Environmental Science / Faculty of Environmental Earth Science) > 雑誌発表論文等 (Peer-reviewed Journal Articles, etc)

Export metadata:

OAI-PMH ( junii2 , jpcoar_1.0 )

MathJax is now OFF:


 - Hokkaido University